Beyond the joint: Measurement and treatment of sensitisation in patients undergoing total knee of hip arthroplasty
Bibliographic record
Abstract
Osteoarthritis is one of the most common causes of pain. Why and how osteoarthritis leads to so much pain in some people is still not fully understood. In a number of people, the pain does not even go away after a hip or knee replacement. This kind pain is a complicated process involving many different factors, including increased pain sensitivity of the nerves and brain. This is called sensitization. The aim of this thesis was to investigate the measurement of signs of sensitization in people with hip or knee osteoarthritis. For this we have created a Dutch version of a reliable and representative questionnaire for signs of sensitization, specifically for patients with hip or knee osteoarthritis. In addition, we investigated whether targeted treatment of sensitization reduces the pain after a hip or knee replacement. The treatment consisted of duloxetine, a pain reliever that works on the brain's processing of pain. Osteoarthritis patients from the Medical Center Leeuwarden, the Martini Hospital and the UMCG participated. No effect of duloxetine treatment was found on the amount of pain remaining in patients after total hip or knee replacement. We did find that the questionnaire used to measure pain after hip or knee replacement was good at measuring change over time. We also found that after a hip or knee replacement, patients' perception of pain changes, as if their internal pain measure is being reset. This is important to consider in future studies examining pain after hip or knee replacement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".